# Teaching

**STAT0024 Social Statistics - Second term 2018-2019**The aim is to provide an introduction to several aspects of sample surveys, namely: survey design and evaluation, statistical sampling theory, measurement in social science and some practical and philosophical aspects.

On successful completion of the course, a student should have an understanding of the basic principles and methods underlying sample surveys, social measurement and scaling, sampling schemes, and practical survey methods, together with an appreciation of the role of statistics in society and social science.

Course content: The role of statistics in social science. Planning a survey, questionnaire construction and data collection techniques. Social measurement and scaling. Sources of error, practical survey methods. Basic data presentation. Sampling theory: simple random sampling, stratified sampling, cluster sampling, and systematic sampling. Analysis of social statistics. Methods for handling missing data.

The course notes will be on the course Moodle page. However, successful participation requires more effort than just 'learning the notes'. Your own thinking and decisions are needed too. Many issues that you will encounter in the real world cannot necessarily be solved by employing a basic toolbox of statistical techniques and learning some facts.

A 2.5 hour written examination in Term 3, plus a 'take-home' in-course assessment (ICA) given out on 19th February with a deadline of 4pm on 5th March.

Nine sets of exercises. In each exercise session, we shall discuss the topics and set example questions. You should be prepared to participate in the discussion, either individually or as a group. These will be discussed in problem classes to provide feedback and from time-to-time you may be asked to present your results to the rest of the class, but this will not count towards the examination grading.

The course notes, examples sheets, ICA information and other material shall be available from the course Moodle page.

**Monte Carlo Inference - Lent 2016**Monte Carlo methods are concerned with the use of stochastic simulation techniques for statistical inference. These have had an enormous impact on statistical practice, especially Bayesian computation, over the last 20 years, due to the advent of modern computing architectures and programming languages. This course covers the theory underlying some of these methods and illustrates how they can be implemented and applied in practice.

The course will cover the following topics:

Techniques of random variable generation

Monte Carlo integration

Importance Sampling

Markov chain Monte Carlo (MCMC) methods for Bayesian inference

Gibbs sampling

Metropolis-Hastings algorithm

Applications in network analysisLecture notes will be given on the board. Further notes may be obtained from Alexandra Carpentier's page on this course, written in 2014. However, notice that more material will be covered in our course.

Two example sheets will be provided and two associated examples classes will be given:

Example Sheet 1: The associated example class will be held on Thursday 25 February, 9-10am, MR13.

Example Sheet 2: The associated example class will be held on Friday 22 April, 9-11am, MR13. (Minor edits on the file on 6 March)

Relationship between distributions (not made by me).

R codes (Mostly written by Shahin Tavakoli) Data generation , MC integration , Metropolis-Hastings algorithm, Gibbs sampling.

Further reading:

P. J. E. Gentle,

*Random Number Generation and Monte Carlo Methods, (Second Edition).*Springer, 2003.B. D. Ripley,

*Stochastic Simulation.*Wiley, 1987.C.P. Robert and G. Casella,

*Monte Carlo Statistical Methods, (Second Edition).*Springer, 2004.C. P. Robert and G. Casella,

*Introducing Monte Carlo Methods with R.*Springer, 2010.**Graphical Models - Spring 2015**This course introduces the theory of Graphical Models to graduate students with sufficient mathematical and statistical background. The main goal is to explore the theory behind using the main types of graphical models in the literature, and their relationship to other statistical models and methods used in statistics. An important part of the course will discuss conditional independence in probability distributions and how different types of graphs can capture them. Although, the course, for the most part, will consist of theory, it will also demonstrate through the use of various examples how to fit appropriate models by using known algorithms in the field.

The course will cover the following topics:

Graphs and conditional independence

Markov properties for undirected graphs

Log-linear models

Decomposable models

Markov properties for directed acyclic graphs

Multivariate regression analysis and graphs

Gaussian graphical models

Graphical models for marginal independence

Markov properties for mixed graphs

Marginalization and conditioning for DAGs

This is the second part of the course "Time Series and Monte Carlo Inference".